Preventing Insurance Fraud & Risk with Real-Time Intent Scoring.  

ForMotiv helps carriers predict and, more importantly, prevent risky and fraudulent applicant approvals by dynamically adding friction during the application process. 

insurance agent application fraud
“Combining ForMotiv’s behavioral data with our internal models led to a drastic improvement in a variety of risk models we were running. Honestly, I was shocked. I had no idea behavioral data could be this powerful.” -Chief Data Science Officer of Top 10 Life Carrier
Behavioral Features Captured Per Application
Micro-Expressions Captured Per Application
Average Risk Model Improvement
Applications Analyzed

Predict Insurance Risk & Fraud with Real-Time Intent Scoring

As companies continue to accelerate their digital transformations and move their user experiences online, it’s easier than ever for risky and fraudulent applications to sneak past their defenses. 

At ForMotiv, we understand that a balance needs to be struck between a seamless user experience and a well-protected application – why not have your cake and eat it too? ForMotiv’s “digital polygraph” analyzes user behavior and accurately predicts whether it’s a risky or fraudulent application while the user is still in the application. But we don’t stop there, we enable carriers to dynamically add or remove friction tailored to the individual user. As carriers move towards consumer complete and accelerated underwriting, having the ability to intuitively triage a risky application can make the difference between a profitable quarter or one in the red. 

predict insurance fraud

Improve Risk & Fraud Models

ForMotiv combines AI/ML with digital behavioral science to create more accurate risk models.

Our solution allows for real-time risk signals or scoring that can be consumed offline or real-time to dynamically intervene when an application is showing signs of risky behavior allowing you to further qualify an applicant before approving their policy. 

predict insurance fraud

Predict & Prevent Application Nondisclosure

Did you know ~50% of digital applicants lie about their smoking status?

Whether you’re looking to identify medical/tobacco nondisclosure, someone fidgeting their application to receive a better quote, or outright misrepresentation by a user or agent, ForMotiv can help. 

Identify Bots

Many carriers have experienced pre-fill fraud recently, so we built a way to stop it.

ForMotiv analyzes “digital body language” to identify a genuine application versus a bot programmed to scrape pre-fill data. Better yet, we enable carriers to intuitively hide or show pre-fill information accordingly. 

life insurance fraud detection

Example Use Cases for Insurance Analytics

insurance digital experience
Marketing & CX
  • Lead Scoring
  • Remarketing Intelligence
  • Dynamic Experiences
insurance agent application fraud
Risk & Fraud
  • Real-Time Risk Scoring
  • Predict Nondisclosure
  • Bot Detection
  • Accelerated Underwriting
  • Intent Scoring
  • Dynamic Friction
Data Science
  • Digital Behavioral Science for Intent Scoring
  • Raw Behavioral Data
  • Real-Time Deterministic Signals
Agent Distribution
  • Lead Scoring & Prioritization
  • Agent “Gaming”
  • Agent Benchmarking
claims analytics
Claims Analytics
  • Predict FNOL
  • Claims Fraud
  • Claims UX

Easy to Integrate. Easy to Use. Totally Safe & Secure.

formotiv integration
Easy Integration

ForMotiv’s JS Snippet embeds inside of your Tag Manager, directly in your website, or through our API and instantly begins tracking form inputs.

Easy to Use

24/7 access to the ForMotiv portal gives your team real-time data analytics on each individual applicant. Drill down on specific applications, questions, and user actions.

Zero PII Collected

Your data is totally secure as ForMotiv collects zero Personal Identifiable Information (PII).

Own Your Data

Your data is just that, yours. ForMotiv provides you access to all of the raw data collected. No customer or outcome information is used with other customers.

Black boxes are annoying. At ForMotiv, we're fans of the glass-box approach.